Complex decision making processes regarding an application feature usually involves consideration of multiple criteria by many users, each user having unique opinions and vantage points. One technique for assessing the extent to which an application feature is successful is to ask the opinions of a community of users that use the feature. The responses can then be quantified and weighted based on respondent feedback, respondent backgrounds, and inputs from a manager of the feature. Finally, the quantified and weighted responses can be aggregated and processed through a collaborative decision engine to provide an overall picture of how the feature is perceived by the community of users. This collaborative approach to decision making allows the manager of the feature to better assess the feature's usefulness, efficiency, importance, and other qualities of the feature.
The collaborative decision engine approach is data intensive because it involves quantifying and weighting every response for each user in the community and then aggregating the quantified and weighted responses for every aspect of the feature. As the number of users in the community grows and as the number aspects of the feature that are evaluated by each user increases, the computational resources required by the collaborative decision engine to handle the workload increases drastically. Additionally, it is difficult and time consuming to have every individual user in the community of users of a feature to respond to surveys related to a feature every time a feature is assessed.